Mental health disorders exact very high personal, social and economic costs in our country and around the world, presenting a significant public health challenge. Clinicians and researchers are currently faced with the difficult task of inferrig patient behavior and treatment adherence between clinic visits through self-report and historical behavior. Cogito's mobile sensing platform Cogito Companion objectively measures behavioral patterns via mobile phone sensors and uses these patterns as inputs to predictive models, trained against clinical outcomes. The models predict mental states, such as components of depression, distress, and anxiety. These predictions can then be presented to patients via their mobile phone and clinicians via a clinical dashboard. Monitoring, analyzing, and visualizing changes in quasi-real time allow clinicians a new window into understanding patient behavior. The storage, aggregation and analysis of these novel signals across groups allows for results providing powerful, generalizable, population-level information. This Phase II project will include a clinical trial validating the efficacy of the technology in a patient-centerd medical home with patients who have comorbid behavioral health conditions. Through implementation of this technology into the workflow of an integrated behavioral health program, results will be gathered on the efficacy of the technology as evaluated by the impact on provider workflow, treatment outcome, patient outcome, self-help behaviors, clinical research, and the upward trend in costs. This validation will lead to a successful Phase III commercialization of the technology.